DriveFace: A Cross-Spectral Through-Glass Face Dataset for On-the-Move Vehicular Border Control
This dataset addresses a gap in biometric authentication for border control, providing a realistic benchmark for developing face recognition methods in vehicular settings.
The authors introduce DriveFace, a cross-spectral through-glass face dataset for on-the-move vehicular border control, and show that state-of-the-art models perform poorly under these realistic conditions, highlighting the need for dedicated methods.
The continuous growth in cross-border mobility places increasing pressure on existing border control infrastructures, motivating on-the-move biometric authentication, in which travellers are identified directly inside their vehicles at checkpoints. Face recognition is well-suited to this setting, as it can be acquired passively and at a distance. Its development, however, is hindered by the lack of representative datasets: existing benchmarks are collected in controlled environments and do not capture the challenges inherent to vehicular acquisition, including motion blur, variable illumination, occlusions, and cross-spectral enrollment. To address this gap, we introduce a dataset for on-the-move face recognition in border-control scenarios, comprising NIR vehicle-crossing videos paired with smartphone-based pre-enrollment data. Baseline evaluations with state-of-the-art models show clear performance limitations under these realistic conditions, highlighting the need for dedicated methods to advance the field.